blackSemantic Segmentation.
Priors from semantic segmentations show good performance in inpainting [1]}, [2]}. But such priors are largely influenced by corrupted images [2]}. Here we leverage an off-the-shelf ADE20K [4]} pre-trained segmentation SETR model [5]} to extract ground truth segmentation priors \(\mathbf {\hat{P}}_{s}\)  from unmasked images. The masked priors \(\mathbf {P}_{s}\)  are directly masked from \(\mathbf {\hat{P}}_{s}\) . Particularly, note that such a prior setting can only serve as the performance upper bound of segmentation priors, in order to help understand how much segmentation can help inpainting.
The number of SETR categories is reduced from 150 to 20 with threshold 0.0115 for the label frequency in Places2 subset (Sec. REF ). The rest 130 categories are merged into another new label (21 categories at all).
The CE is used to optimize the generated \(\mathbf {\tilde{P}}_{s}\) .
